# Decentralized Task and Path Planning for Multi-Robot Systems

arXiv: https://arxiv.org/abs/2011.10034 · HTML: https://mlsanity.com/paper/2011.10034/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments](https://mlsanity.com/paper/2209.02865/) (2022)
- [Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning](https://mlsanity.com/paper/2605.27697/) (2026)
- [Together We Rise: Optimizing Real-Time Multi-Robot Task Allocation using Coordinated Heterogeneous Plays](https://mlsanity.com/paper/2502.16079/) (2025)
- [ROSClaw: A Hierarchical Semantic-Physical Framework for Heterogeneous Multi-Agent Collaboration](https://mlsanity.com/paper/2604.04664/) (2026)
- [Robot guide with multi-agent control and automatic scenario generation with LLM](https://mlsanity.com/paper/2509.10317/) (2025)

## Abstract (arXiv)

We consider a multi-robot system with a team of collaborative robots and multiple tasks that emerges over time. We propose a fully decentralized task and path planning (DTPP) framework consisting of a task allocation module and a localized path planning module. Each task is modeled as a Markov Decision Process (MDP) or a Mixed Observed Markov Decision Process (MOMDP) depending on whether full states or partial states are observable. The task allocation module then aims at maximizing the expected pure reward (reward minus cost) of the robotic team. We fuse the Markov model into a factor graph formulation so that the task allocation can be decentrally solved using the max-sum algorithm. Each robot agent follows the optimal policy synthesized for the Markov model and we propose a localized forward dynamic programming scheme that resolves conflicts between agents and avoids collisions. The proposed framework is demonstrated with high fidelity ROS simulations and experiments with multiple ground robots.